mirror of
https://github.com/Nighthawk42/mOrpheus.git
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93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
# modules/snac_decoder.py
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import time
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import torch
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import numpy as np
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from modules.logging import logger
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from snac import SNAC # Ensure that the snac module is installed
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# Load SNAC model
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snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
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snac_device = "cuda" if torch.cuda.is_available() else "cpu"
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logger.info("Using SNAC on device: %s", snac_device)
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snac_model = snac_model.to(snac_device)
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cuda_stream = torch.cuda.Stream() if snac_device == "cuda" else None
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def convert_to_audio(multiframe, count):
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if len(multiframe) < 7:
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return None
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num_frames = len(multiframe) // 7
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frame = multiframe[:num_frames * 7]
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codes_0 = torch.zeros(num_frames, dtype=torch.int32, device=snac_device)
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codes_1 = torch.zeros(num_frames * 2, dtype=torch.int32, device=snac_device)
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codes_2 = torch.zeros(num_frames * 4, dtype=torch.int32, device=snac_device)
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frame_tensor = torch.tensor(frame, dtype=torch.int32, device=snac_device)
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for j in range(num_frames):
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idx = j * 7
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codes_0[j] = frame_tensor[idx]
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codes_1[j * 2] = frame_tensor[idx + 1]
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codes_1[j * 2 + 1] = frame_tensor[idx + 4]
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codes_2[j * 4] = frame_tensor[idx + 2]
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codes_2[j * 4 + 1] = frame_tensor[idx + 3]
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codes_2[j * 4 + 2] = frame_tensor[idx + 5]
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codes_2[j * 4 + 3] = frame_tensor[idx + 6]
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codes = [codes_0.unsqueeze(0), codes_1.unsqueeze(0), codes_2.unsqueeze(0)]
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if (torch.any(codes[0] < 0) or torch.any(codes[0] > 4096) or
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torch.any(codes[1] < 0) or torch.any(codes[1] > 4096) or
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torch.any(codes[2] < 0) or torch.any(codes[2] > 4096)):
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return None
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stream_ctx = torch.cuda.stream(cuda_stream) if cuda_stream is not None else torch.no_grad()
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with stream_ctx, torch.inference_mode():
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audio_hat = snac_model.decode(codes)
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audio_slice = audio_hat[:, :, 2048:4096]
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if snac_device == "cuda":
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audio_int16_tensor = (audio_slice * 32767).to(torch.int16)
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audio_bytes = audio_int16_tensor.cpu().numpy().tobytes()
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else:
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audio_np = audio_slice.detach().cpu().numpy()
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audio_int16 = (audio_np * 32767).astype(np.int16)
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audio_bytes = audio_int16.tobytes()
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return audio_bytes
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def turn_token_into_id(token_string, index):
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token_string = token_string.strip()
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if "<custom_token_" not in token_string:
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return None
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last_token_start = token_string.rfind("<custom_token_")
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if last_token_start == -1 or not token_string.endswith(">"):
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return None
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try:
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number_str = token_string[last_token_start + 14:-1]
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return int(number_str) - 10 - ((index % 7) * 4096)
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except (ValueError, IndexError):
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return None
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token_cache = {}
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MAX_CACHE_SIZE = 1000
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def tokens_decoder(token_gen):
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buffer = []
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count = 0
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min_frames_required = 28
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process_every = 7
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for token_text in token_gen:
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cache_key = (token_text, count % 7)
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if cache_key in token_cache:
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token = token_cache[cache_key]
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else:
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token = turn_token_into_id(token_text, count)
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if token is not None and len(token_cache) < MAX_CACHE_SIZE:
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token_cache[cache_key] = token
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if token is not None and token > 0:
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buffer.append(token)
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count += 1
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if count % process_every == 0 and count >= min_frames_required:
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buffer_to_proc = buffer[-min_frames_required:]
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audio_samples = convert_to_audio(buffer_to_proc, count)
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if audio_samples is not None:
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yield audio_samples
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def tokens_decoder_sync(syn_token_gen):
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audio_segments = list(tokens_decoder(syn_token_gen))
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return b"".join(audio_segments)
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